Far-field pattern measurement is commonly used to characterize the behavior of antennas. Far-field pattern measurement typically comprises measurement of transmitted power at different angles (e.g., azimuth, elevation) within the far-field of an antenna, and is commonly used in antenna development and testing to ensure that the antenna meets desired performance specifications.
Far-field pattern measurement is typically performed in an anechoic chamber to ensure precision and reliability of the resulting data. An anechoic chamber, however, is generally large, expensive to construct, and expensive to operate. In addition, pattern acquisition time is typically extremely slow. For example, a single cut plane scan (e.g., constant azimuth or elevation angle) at a single frequency may take twenty minutes if measurements are performed at angular intervals of 1°. Moreover, during a test, an antenna designer may wish to measure the far-field pattern of an antenna at a number of frequencies and may want a more complete pattern characterization such as a spherical plot covering both azimuth and elevation angles. This capability requires multi-axis gimbaling, which many facilities lack. Furthermore, such a set of two dimensional (2D) pattern measurements may take days. As certified testing facilities may be expensive (e.g., $10,000 per day), this presents a serious financial barrier to iterative product development.
There are various alternatives to using an anechoic chamber. For example, some companies offer near-field pattern measurement systems whereby a receiver probe antenna is mechanically scanned across an antenna under test (AUT) and well-known mathematical techniques are used to convert the measurements into far-field data. However, one must still be careful to provide “anechoic-like” conditions. Accordingly, the near-field pattern measurement systems generally use one of three types of geometry for both scanning and an anechoic absorber layout, including planar, cylindrical, and spherical geometries. A significant benefit of near-field systems is their compactness relative to an anechoic chamber. However, due to the mechanical scanning, the near-field pattern measurements are still rather slow.
Due to high cost and slow performance, conventional approaches to far-field pattern measurement may be impractical for newer antenna technologies such as smart antennas. One reason for this impracticality is that smart antennas may need to be characterized under rapidly changing conditions to ensure proper performance in a real-world environment. For example, a smart antenna generally comprises an antenna array that can adapt its beam to handle obstructions or multipath communication, both of which may change frequently. In one example, a smart antenna and a receiver form a directional link that is temporarily blocked by an intervening obstacle such as a passing body or vehicle. To avoid the obstacle, the smart antenna may temporarily redirect its beam to bounce off of a wall and propagate to the receiver. This can be accomplished, for instance, by adjusting transmission characteristics of individual elements of the antenna array. One way to adjust these transmission characteristics is by specifying different weight vectors for the array elements to achieve different beam patterns.
Such adjustments may be required every few seconds or even milliseconds according to various changes in the environment. Accordingly, to ensure reliable operation, the antenna's far-field pattern may need to be characterized under these rapidly changing conditions. In other words, it may be insufficient to characterize a smart antenna under static measurement conditions because unexpected glitches or other phenomena may arise during dynamic operation. Conventional methods, however, are unable to measure 2D or even 1D antenna patterns with the speed required by a dynamic real-world environment.
Another reason conventional approaches to far-field antenna measurement may be impractical for smart antennas is that these types of antennas may need to be characterized with respect to a large number of transmitted patterns. Performing a large number of measurements, however, may require too much time with conventional approaches. For example, in a WiGIG/Wireless HD protocol there are forty eight transmitter weight vectors and forty eight receiver weight vectors to explore. Each weight vector represents a new pattern and the protocol actually asks for several rounds of exploration (iteration) before a new link is established.
In view of the above shortcomings of conventional technologies, there is a general need for faster approaches to far-field pattern measurement, especially for dynamic applications such as smart antennas.